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Healthcare Analytics for Quality
and Performance Improvement



Healthcare Analytics for
Quality and Performance
Improvement

TREVOR L. STROME


Cover image: © iStockphoto.com/pictafolio
Cover design: Andrew Liefer
Copyright © 2013 by Trevor L. Strome. All rights reserved.
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Library of Congress Cataloging-in-Publication Data:
Strome, Trevor L., 1972–
Healthcare analytics for quality and performance improvement / Trevor L. Strome.
pages cm
ISBN 978-1-118-51969-1 (cloth) — ISBN 978-1-118-76017-8 (ePDF) —
ISBN 978-1-118-76015-4 (ePub) — ISBN 978-1-118-761946-1 (oBook). 1. Health
services administration—Data processing. 2. Information storage and retrieval
systems—Medical care. 3. Organizational effectiveness. I. Title.
RA971.6.S77 2014
362.1068—dc23
2013023363
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1

Dedicated to
Karen, Isabella, and Hudson—for all your support,
understanding, and love



Contents

Preface

ix

Acknolwedgments
CHAPTER 1

CHAPTER 2

CHAPTER 3

CHAPTER 4

CHAPTER 5

xiii

Toward Healthcare Improvement Using Analytics

1

Healthcare Transformation—Challenges

and Opportunities
The Current State of Healthcare Costs and Quality

1
3

Fundamentals of Healthcare Analytics

15

How Analytics Can Improve Decision Making
Analytics, Quality, and Performance
Applications of Healthcare Analytics
Components of Healthcare Analytics

15
17
19
21

Developing an Analytics Strategy to Drive Change

29

Purpose of an Analytics Strategy
Analytics Strategy Framework, with a Focus on
Quality/Performance Improvement
Developing an Analytics Strategy

29

Defining Healthcare Quality and Value

51

What Is Quality?
Overview of Healthcare QI
Common QI Frameworks in Healthcare
Working with QI Methodologies

51
59
61
73

Data Quality and Governance

75

The Need for Effective Data Management
Data Quality
Data Governance and Management
Enterprise-wide Visiblilty and Opportunity

76
78
84
88

32

47

vii


viii

CHAPTER 6

CHAPTER 7

CHAPTER 8

CHAPTER 9

Contents

Working with Data
Data: The Raw Material of Analytics
Preparing Data for Analytics
Getting Started with Analyzing Data
Summary

92
92
100
112

Developing and Using Effective Indicators

115

Measures, Metrics, and Indicators
Using Indicators to Guide Healthcare
Improvement Activities

115
123

Leveraging Analytics in Quality Improvement Activities

129

Moving from Analytics Insight to
Healthcare Improvement

129

Basic Statistical Methods and Control Chart Principles

145

Statistical Methods for Detecting Changes
in Quality or Performance
Graphical Methods for Detecting Changes in
Quality or Performance
Putting It Together
CHAPTER 10

CHAPTER 11

CHAPTER 12

91

145
153
160

Usability and Presentation of Information

165

Presentation and Visualization of Information
Dashboards for Quality and Performance
Improvement
Providing Accessibility to and Ensuring Usability
of Analytics Systems

165

180

Advanced Analytics in Healthcare

183

Overview of Advanced Analytics
Applications of Advanced Analytics
Developing and Testing Advanced Analytics

Overview of Predictive Algorithms

183
186
190
197

Becoming an Analytical Healthcare Organization

205

Requirements to Become an Analytical Organization
Building Effective Analytical Teams
Summary

207
213
215

173

About the Author

217

About the Companion Web Site

219

Index

221


Preface

Why write a book on healthcare analytics that focuses on quality and performance improvement? Why not focus instead on how healthcare information technology (HIT) and “big data” are revolutionizing healthcare, how
quality improvement (QI) methodologies such as Lean and Six Sigma are
transforming poorly performing healthcare organizations (HCOs) into bestin-class facilities, or how leadership and vision are the necessary driving
factors behind innovation and excellence within HCOs?
The truth is, this book is about all these things. Or, more accurately, this
book is about how healthcare organizations need to capitalize on HIT, data
from source systems, proven QI methodologies, and a spirit of innovation
to achieve the transformation they require. All of these factors are necessary
to achieve quality and performance improvement within modern healthcare
organizations. However, the professionals working in healthcare IT, quality improvement, management, and on the front lines all speak different
languages and see the world from different perspectives—technology, data,
leadership, and QI. This gap (a chasm, really) prevents these professionals
from effectively working together and limits their capability to perform
effective quality and performance improvement activities. This may in fact
be lowering the quality of care and decreasing patient safety at a time when
doing the opposite is critical.
This book demonstrates how the clinical, business, quality improvement, and technology professionals within HCOs can and must collaborate.
After all, these diverse professional groups within healthcare are working together to achieve the same goal: safe, effective, and efficient patient
care. Successful quality improvement requires collaboration between these
different stakeholders and professional groups; this book provides the
common ground of shared knowledge and resources necessary for QI, IT,
leadership, and clinical staff to become better coordinated, more integrated,
and to work together more effectively to leverage analytics for healthcare
transformation.

ix


x

Preface

In this book, I hope to demonstrate that analytics, above all, can and
must be made accessible throughout the entire HCO in order for the insight
and information possible through analytics to actually get used where it is
needed. I attempt to dispel the myth that only a select few can be qualified
to be working with the data of an HCO. Although the process of generating insight through analytics requires some statistics and mathematics, the
output or result of analytics must make intuitive sense to all members of the
healthcare team. In my experience, if the information and insight produced
by business intelligence and analytics is too complex to understand for all
but the team that generated it, then that information will contribute very
little to healthcare improvement.
In keeping with the theme of accessibility, I have attempted to keep
this book very accessible to readers with various backgrounds and experience. The book covers a wide range of topics spanning the information
value chain, from information creation and management through to analysis, sharing, and use. As such, it cannot cover each of the topics completely
and in depth. But it does cover the areas that I believe are vital in a quality
improvement environment driven by analytics. If you work in the area of
health IT, data management, or QI, I have attempted to connect the dots
in how your professional discipline fits in with the others. I hope that this
book can thereby enable technical, analytical, QI, executive, and clinical
members of the healthcare team to communicate clearly, better understand
one another’s needs, and jointly collaborate to improve the efficiency, effectiveness, and quality of healthcare.
I do admit my bias toward the acute-care setting, and emergency
departments in particular. The vast majority of my career has been within

acute care and emergency, and the writing and examples in this book definitely reflect that bias—although I have tried not to make every example
an emergency department example! The basic concepts of quality, value,
performance, and analytics will translate well to almost any setting, whether
it is medicine, surgery, home care, or primary care.
In my opinion, the real value of analytics occurs when the insight generated through analytical tools and techniques can be used directly by quality improvement teams, frontline staff, and other healthcare professionals to
improve the quality and efficiency of patient care. To some, this may not
be the most glamorous application of analytics, but it is the most important.

Book Overview
After a discussion of the escalating inefficiencies and costs of healthcare
(Chapter 1), a high-level overview of the various components of an effective analytics system within an HCO is covered in Chapter 2. Because of the


Preface

xi

need for strong alignment between the quality and process improvement
goals of the organization, the various demands facing healthcare IT departments, and the balancing that analytics must do between these competing
interests, Chapter 3 provides an overview of an effective analytics strategy
framework that HCOs can use to keep their focus on efforts that achieve the
desired improvement results of the organization. Chapter 4 is an overview
of the concepts of quality and value, and how these are measured within
an HCO. Three quality improvement methodologies (PDSA, Lean, and Six
Sigma) are discussed in Chapter 4 as well, and how analytics can provide
support to these various types of initiatives.
Chapters 5, 6, and 7 focus on data. Chapter 5 is an overview of data
quality and data management, and how to ensure that analytics professionals and stakeholders have access to the high-quality data they need in order
to provide information and insight to the organization. Chapter 6 discusses
the different types of data, important methods of summarizing and understanding data, and how data type affects the kind of analysis that is possible.

Chapter 7 provides tips on how to convert data into metrics and indicators
that provide the HCO with a much clearer lens through which to monitor
and evaluate performance and quality.
Chapter 8 is about how to meld analytics and quality improvement
activities so that QI teams can benefit from the insight and information
available throughout all phases of QI projects, regardless of the QI methodology that is chosen. Chapter 9 highlights several of the key statistical and
graphical methods for monitoring performance and detecting when in fact a
true change in performance or quality has occurred. Chapter 10 talks about
usability of analytics from an access and presentation point of view. The
advanced analytics discussed in Chapter 11 includes tools such as regression and machine-learning approaches that can be used to identify patterns
in healthcare data and predict likely outcomes.
Finally, Chapter 12 discusses achieving analytics excellence within an
HCO, including the types of leadership and management required within
an HCO to ensure that data and privacy are held secure and that analytics
is used appropriately and to its maximum effectiveness.



Acknowledgments

It is impossible to write a book of this scope without tremendous amounts
of support and encouragement. I am lucky to be surrounded by people who
have been incredibly encouraging and supportive throughout this journey.
First and foremost, I would like to thank my wife and my two wonderful children for your unconditional love and support, and for your inspiration and undying encouragement during the writing of this book. I love you
more than you can ever know!
I would like to thank my friends and colleagues at the Winnipeg Regional Health Authority (WRHA) Emergency Program, within other WRHA
departments and programs, and in the Department of Emergency Medicine,
University of Manitoba. The support, guidance, and feedback you’ve given
me during the writing process were absolutely instrumental in helping me
complete this work. I have gained tremendously by working on frontline

quality improvement projects with many of the hardest-working and most
dedicated clinical personnel in healthcare. To everyone from whom I’ve
drawn the examples and case studies in this book, it is from your experience, efforts, and desire to improve healthcare that I gain confidence that
healthcare transformation is truly possible.
I would like to thank Karen Strome, Lori Mitchell, and Ryan McCormack,
who provided invaluable assistance by reviewing and commenting on several of the key chapters in this book. Your advice and feedback have made
this a much better book than would have been possible on my own.
I would also like to thank Laura Madsen, preeminent healthcare business intelligence expert and author of Healthcare Business Intelligence: A
Guide to Empowering Successful Data Reporting and Analytics, for inspiring
me to write this book and for kindly introducing me to her publisher, John
Wiley & Sons.

xiii



CHAPTER

1

Toward Healthcare
Improvement Using Analytics
Innovation is anything but business as usual.
—Anonymous
How sustainable is healthcare in its current state? Most healthcare organizations (HCOs) claim to be undertaking quality improvement (QI) initiatives,
but only a few are consistently improving the quality of healthcare in a
sustainable fashion. Despite increased spending on healthcare in the United
States, there is little evidence that the quality of healthcare can be improved
by increasing spending alone. Health information systems is one technology
with the potential to transform healthcare because, among its many capabilities, it can deliver the best evidence to the point of care, employs intelligent

algorithms to reduce and prevent medical mistakes, and collects detailed
information about every patient encounter. Even with growing volumes of
data to analyze resulting from the continuing proliferation of computer systems, HCOs are struggling to become or remain competitive, highly functioning enterprises. This chapter will highlight current challenges and pressures facing the healthcare system, identify opportunities for transformation,
and discuss the important role that analytics has in driving innovation and
achieving healthcare transformation goals.

Healthcare Transformation—Challenges
and Opportunities
Healthcare delivery is undergoing a radical transformation. This is occurring as the result of both necessity and opportunity. Change is necessary
1


2

Toward Healthcare Improvement Using Analytics

because, in many ways, the provision of healthcare is less efficient, less
safe, and less sustainable than in the past. The opportunity, however,
arises from the advancement of technology and its impact on healthcare
delivery. Technology now allows increasingly intelligent medical devices
and information systems to aid in clinical decision making, healthcare
management, and administration. The challenge facing HCOs is to leverage advances in both clinical device technology and information technology (IT) to create and sustain improvements in quality, performance,
safety, and efficiency.
Data generated via healthcare information technology (HIT) can help
organizations gain significantly deeper insight into their performance than
previous technologies (or lack of technology) allowed. HCOs, however,
face the very real risk of information overload as nearly every aspect of
healthcare becomes in some way computerized and subsequently datagenerating. For example, radio frequency identification (RFID) devices can
report the location of every patient, staff member, and piece of equipment
within a facility; sampled every second, the location data captured from

these devices accumulates quickly. Portable diagnostic equipment now captures and stores important patient clinical data, such as vital signs, and can
forward that data to electronic medical records (EMRs) or other computerized data stores. Similarly, devices with embedded “labs on a chip” can
now perform point-of-care testing for many blood-detectable diseases, and
generate enormous volumes of data while doing so.
HCOs must find a way to harness the data at their disposal and take
advantage of it to improve clinical and organizational performance. Data
analytics is critical to gaining knowledge, insight, and actionable information from these organizations’ health data repositories. Analytics consists of the tools and techniques to explore, analyze, and extract value
and insight from healthcare data. Without analytics, the information and
insight potentially contained within HCOs’ databases would be exceedingly difficult to obtain, share, and apply.
But insight without action does not lead to change; data overload can
risk impeding, not improving, the decision-making ability of healthcare
leaders, managers, and QI teams. In my experience, the true potential of
analytics is realized only when analytics tools and techniques are combined
with and integrated into a rigorous, structured QI framework. This powerful combination helps to maintain the focus of QI and management teams
on achieving the quality and business goals of an organization. Analytics
can also be used to explore the available data and possibly identify new
opportunities for improvement or suggest innovative ways to address old
challenges. When an HCO uses analytics to focus improvement efforts on
existing goals and to identify new improvement opportunities, healthcare
can become more effective, efficient, safe, and sustainable.


The Current State of Healthcare Costs and Quality

3

The Current State of Healthcare Costs and Quality
A discussion on the topic of healthcare analytics must first begin with a discussion of healthcare quality. This is because analytics in healthcare exists for the
purpose of improving the safety, efficiency, and effectiveness of healthcare delivery. Looking at the current and emerging challenges facing healthcare the way
we looked at problems in the past can and will only result in more of the same.

And it seems that many people, from healthcare providers who are overworked
to patients who must endure unacceptably long waiting lists for relatively common procedures, are extremely dissatisfied with the way things are now.
Despite the seemingly miraculous capabilities of the healthcare system to
maintain the health of, and in many cases save the lives of, patients, the system itself is far from infallible. The question of how safe is healthcare delivery
must continually be asked. The often-cited Institute of Medicine (IoM) report
To Err Is Human: Building a Safer Health System declares that a “substantial
body of evidence points to medical errors as a leading cause of death and
injury.”1 The report cites two studies that estimate between 44,000 and 98,000
patients die every year in hospitals because of medical errors that could have
been prevented. These are people who expected the healthcare system to
make them well again or keep them healthy and were horribly let down.
According to the IoM report, the types of errors that commonly occur in
hospitals include “adverse drug events and improper transfusions, surgical
injuries and wrong-site surgery, suicides, restraint-related injuries or death,
falls, burns, pressure ulcers, and mistaken patient identities.” Not surprisingly,
emergency departments, operating rooms, and intensive care units experience the highest error rates and those with the most serious consequences.
Not only do hospital errors result in a staggering yet largely preventable human toll, but they result in a tremendous financial burden as well.
It is estimated that the cost to society of these preventable errors ranges
between $17 billion and $29 billon in both direct and indirect financial
costs. Of course, the majority of these errors are not caused by deliberate
malpractice, recklessness, or negligence on the part of healthcare providers.
Rather, according to the IoM report, the most common causes of healthcare
errors are “due to the convergence of multiple contributing factors” and that
“the problem is the system needs to be made safer.”2
In the near decade and a half that has passed since the release of the
1999 Institute of Medicine report, most of its findings are as relevant today
as they were in 1999. Despite dramatic innovations in biomedicine and
healthcare technology since the IoM report, many HCOs today still find
themselves under immense pressures, some of which include:
Improving quality and patient safety

Ensuring patient satisfaction

■
■


4

Toward Healthcare Improvement Using Analytics

Adapting to changes in legislation and regulations
Adopting new technologies
■
Demonstrating improved patient outcomes
■
Remaining sustainable and competitive
■
■

The challenge facing HCOs today is to balance the need to innovate by
adopting new technologies and improving processes while providing the
essentials of safe, efficient, and effective patient care. While these two needs
are complementary, with improved patient care as the ultimate goal, they
both require financial, human, and technical resources that are drawn from
a limited, and in some cases shrinking, resource pool.

The Cost of Healthcare
HCOs must endeavor to reduce unnecessary deaths, injuries, and other
hardships related to medical errors and other issues stemming from substandard quality. But given that the cost of healthcare delivery seems to be
increasing unabatedly, could healthcare be at risk of becoming unsustainable in its current form? Direct and indirect costs attributed to healthcare

represent a significant and increasing burden on the economies of countries providing modern healthcare, and may not be sustainable at current
growth rates.
Figure 1.1 illustrates the immense cost of healthcare by showing
the percentage of healthcare expenditures as a proportion of the gross
domestic product (GDP) of selected countries.3 Of the countries in Figure 1.1, total health expenditure as a share of GDP ranges from 2.4
percent (Indonesia) to 17.4 percent (United States). Of significance is
that healthcare expenditures in the United States totaled over 17 percent
of its GDP—5 percent more than the next highest country, and almost
8 percent more than the OECD average of 9.6 percent. But not only
have expenditures on healthcare increased in the United States from
approximately 5 percent of GDP in 1960 to over 15 percent in 2008, they
are expected to grow still further, reaching approximately 20 percent of
GDP by 2018.
Andy Grove, former chief operating office and chief executive officer
of Intel Corporation and a pioneer in the semiconductor industry, once
stated, “There is at least one point in the history of any company when
you have to change dramatically to rise to the next level of performance.
Miss that moment—and you start to decline.” Given the numerous pressures and escalating costs facing the healthcare systems of many nations,
now is the time for HCOs to innovate using available tools and technologies to transform into more sustainable, efficient, effective, and safe providers of care.


The Current State of Healthcare Costs and Quality

Public

% of GDP

5

Private

18

17.4

20

16

4.2

5.4

4.6

6

6.4
6.1

7.4
7.0
6.9

7.8
7.4
Luxembourg3
Hungary

8.2

7.9

8

Israel

8.7
8.5
8.5
8.4

9.3

9.2
9.1
9.0

9.5
9.5

9.6
9.6
9.5

9.6

10.0

9.8

9.7

11.4
11.0
10.9
10.3
10.1

10

11.4

11.6
11.5

12

12.0
11.8

14

2.4

4

FIGURE 1.1  Total Healthcare Expenditures for Selected Countries as a Share of
Gross Domestic Product (2009)
1. In the Netherlands, it is not possible to clearly distinguish the public and private
share related to investments.

2. Total expenditure excluding investments.
3. Health expenditure is for the insured population rather than the resident population.
Source: OECD Health Data 2011; WHO Global Health Expenditure Database.

The Analytics Opportunity in Healthcare
The good news is that HCOs can take the necessary action to improve quality of care, increase value to patients, and raise the bottom line. Advances
in HIT, and particularly the field of healthcare analytics, are now helping
HCOs to reveal and act on opportunities for transformative improvement.
The term “analytics” has been described in myriad ways. For the purposes of this book, I will refer to analytics as the systems, tools, and techniques that help HCOs gain insight into current performance, and guide
future actions, by discerning patterns and relationships in data and using
that understanding to guide decision making. Analytics enables leaders,
managers, and QI teams within HCOs to make better decisions and take
more appropriate actions by providing the right information to the right
people, at the right time, in the right format, with the right technology.

Indonesia

Russian Fed.
China
India

Turkey

Korea
Mexico

Poland
Estonia

Chile

Czech Rep.

South Africa

Brazil
Australia
Japan

Slovenia
Finland
Slovak Rep.

Spain

OECD
Ireland
Italy

Iceland
Greece
Norway

Sweden
United Kingdom

Belgium2
New Zeland
Portugal

Denmark

Canada
Switzerland
Austria

France
Germany

0

United States
Netherlands1

2


6

Toward Healthcare Improvement Using Analytics

Healthcare Analytics
Healthcare analytics consists of the systems, tools, and techniques that
help HCOs gain insight into current performance, and guide future actions, by discerning patterns and relationships in data and using that understanding to guide decision making.
One doesn’t need to look far to observe the impact that analytics has
had on other industries. Companies such as Google, Amazon, and others
whose very existence depends on users’ ease of access to highly targeted,
tailored, and user-friendly information demonstrate the realm of the possible—that the tools, techniques, algorithms, and data now exist to drive our
analytics-powered world.
The use of analytics in healthcare, however, has lagged behind other
industries. Internet search engines make it incredibly easy to enter a search
term and almost immediately retrieve a list of web pages that contain information pertaining to the search term ranked in order of relevance and likely

usefulness. Yet anyone who has used an EMR or a reporting tool to look
up information on a patient, or a group of patients, knows how difficult
finding the necessary information can be. And anybody who has tried to
get the information they need for a healthcare quality and/or performance
improvement project would not be faulted for thinking that obtaining any
information of value is downright impossible.
WHY QUALITY IMPROVEMENT PROJECTS FAIL  HCOs are always working to
improve the quality of their care and the efficiency of their business operations. Many HCOs do not see much improvement in quality and performance despite engaging in multiple improvement initiatives. Unfortunately,
some HCOs will undertake QI projects without an overall quality strategy
or long-term evaluation plan and end up with many disconnected, halfevaluated projects that never seem to achieve their objectives.
Some HCOs focus on improving quality in bursts, with intense activity
and enthusiasm that lasts only for a short period of time. Such torrents of
QI activity is usually in reaction to some negative event such as a critical
incident, or after a “eureka” moment occurs in which an executive member
learns something new at a conference, after seeing a product demonstration, or while speaking with a consultant. Once the initial excitement wears
off the initiative, the unit, department, program, facility, or entire enterprise
may revert back to its initial or some other suboptimal state if a solid quality
framework and sustainability plan are not in place.
Even HCOs with QI entrenched in their organizational culture, a proven
track record, and well-evolved QI frameworks in place rarely achieve total


The Current State of Healthcare Costs and Quality

7

success and must revisit areas of improvement (often multiple times) to help
ensure that improvement results are maintained. This is because achieving
change within HCOs is difficult and, much like breaking a bad habit, rarely
is sustained after the first try.

Health care is the most difficult, chaotic, and complex industry
to manage today [and the hospital is] altogether the most complex
human organization ever devised.
—Peter Drucker
Making changes to an HCO is difficult because healthcare is a very
dynamic environment and in a constant state of flux. Innovations in healthcare technology are ushering in changes at a rapid pace, emerging diseases
and changing patient demographics are presenting new treatment challenges to clinical staff, and organizations themselves face an ongoing barrage
of new regulations and changes to funding models. What might have been
an effective and/or necessary process, workflow, or policy 20 years ago
(or even two years ago) may be no longer relevant, or in need of major
updating to be made relevant once again.
HCOs must evolve and adapt not merely to maintain and improve quality, performance, and patient safety, but to survive. Of course, the standard
principles of providing safe, efficient, and effective patient care will never
change—but exactly how that is done must always evolve.
LEVERAGING INFORMATION TECHNOLOGY  Although HIT is one of the
largest drivers of healthcare innovation (or disruption, as some healthcare providers would claim), HIT provides the tools required to monitor,
evaluate, and improve healthcare quickly and with clarity. In fact, improving quality in a modern HCO to the extent and at the pace necessary
without the benefit of the information derived from HIT would be an
onerous task.

A NOTE ON TERMINOLOGY
I will use the term “healthcare information technology” (HIT) when
referring to systems that are mainly clinical in nature such as electronic
medical record (EMR), radiology information system (RIS), and other
similar systems. I will use the term “information technology” (IT) more
generically to include both clinical and nonclinical systems (such as
financial, supply chain management, and other such tools).


8

Toward Healthcare Improvement Using Analytics

Despite what some vendors may promise, it takes more than simply
adopting HIT to improve quality and performance within an HCO. In fact, it
is ironic that a mere decade ago many healthcare improvement efforts were
likely stymied due to lack of data. Now it is entirely possible that improvement efforts could be hindered by having too much data available without
the necessary experience and tools to analyze it and put it to good use.
This is not to say that healthcare improvement cannot occur without
the use of IT, but at some point every HCO must use data to monitor and
evaluate ongoing changes and fine-tune improvements. I have seen mediocre HCOs become top performers as a result of the intelligent use of information in combination with strong leadership, a clear vision, a culture of
innovation, and a drive to succeed. Although technology is never the only
solution, analytics consists of many tools, technologies, and techniques
that HCOs can employ to leverage the data amassed from the increasing
number of HIT systems in operation. These innovations in combination
with competent, effective leadership enable HCOs to become more efficient and adept at achieving, evaluating, and sustaining improvements in
healthcare.
THE ANALYTICS KNOWLEDGE GAP  In pursuit of clinical and operational
excellence, HCOs are drawing from diverse, nontraditional professions
(from a healthcare perspective) to form QI and innovation teams. In addition to nurses, physicians, and administrators, it is not uncommon to see
engineers, computer scientists, and other specialist roles working within
healthcare. Although having traditional and nontraditional roles working
side by side to solve the many problems facing healthcare brings incredible
diversity and flexibility, this arrangement also poses some challenges.
Successful healthcare quality and performance improvement initiatives
require strong executive sponsorship and support, QI expertise, subject
matter expertise, and information management and analysis expertise.
Bringing these various disciplines together provides diversity that can lead
to the synergistic development of innovations but also exposes significant
knowledge gaps between these groups. (See Figure 1.2 for an illustration of

this knowledge gap.)
Each professional group brings with it its own particular skill sets,
knowledge, and comfort levels working with data and analytics. The analytics knowledge gap may make it seem like nobody is speaking the same
language, which can prevent teams from working effectively and cohesively
together. To reduce friction and misunderstanding on healthcare quality
and leadership teams, it is necessary to bridge the knowledge gap. Bridging
the gap enables team members to communicate more effectively, to ask the
right questions, and to frame the answers and insights in ways that make
sense and are relevant to the improvement challenges at hand.


The Current State of Healthcare Costs and Quality

9

Healthcare
Management
& Leadership

Information Gap

Quality
Improvement

Information
Technology

FIGURE 1.2  The Analytics Information Gap between QI, IT, and Healthcare
Leadership

Leveraging Information for Healthcare Improvement
As HCOs turn to technological solutions to manage business operations
and treat patients, many are literally becoming awash in data. In fact,
some estimates are that healthcare data in the United States alone totaled
approximately 150 exabytes (150 × 1018 bytes) in 2011 for clinical, financial, and administration systems; of course, this number will only continue to grow. In fact, a single large American healthcare provider alone
is estimated to have accumulated up to 44 petabytes (a petabyte is 1015
bytes) of patient data from electronic health record data (including images
and annotations).4
As HCOs continue to amass large quantities of data, that data is only of
any value if it gets used. Many HCOs are becoming more “data centered,” in

“BIG DATA” IS A RELATIVE TERM
Although “big data” is a term commonly used to describe the very large
data sets of today, there is no doubt that the anticipated future growth
in healthcare data will make today’s “big data” seem minuscule. I still
remember when having 16 megabytes of random access memory on a
computer was a big deal, and a 1-gigabyte hard drive was considered
more storage than you’d ever need.